Performance indicators for combinations of machine learning models

ABSTRACT

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may transmit capability information that indicates support for one or more model combinations of machine learning (ML) models, wherein the capability information further indicates one or more performance parameters of an ML model of the ML models with respect to a model combination of the one or more model combinations that includes the ML model. The UE may receive one or more indications to use one or more of the ML models based at least in part on the capability information. Numerous other aspects are described.

FIELD OF THE DISCLOSURE

Aspects of the present disclosure generally relate to wirelesscommunication and to techniques and apparatuses for parameters forcombinations of machine learning models.

BACKGROUND

Wireless communication systems are widely deployed to provide varioustelecommunication services such as telephony, video, data, messaging,and broadcasts. Typical wireless communication systems may employmultiple-access technologies capable of supporting communication withmultiple users by sharing available system resources (e.g., bandwidth,transmit power, or the like). Examples of such multiple-accesstechnologies include code division multiple access (CDMA) systems, timedivision multiple access (TDMA) systems, frequency division multipleaccess (FDMA) systems, orthogonal frequency division multiple access(OFDMA) systems, single-carrier frequency division multiple access(SC-FDMA) systems, time division synchronous code division multipleaccess (TD-SCDMA) systems, and Long Term Evolution (LTE).LTE/LTE-Advanced is a set of enhancements to the Universal MobileTelecommunications System (UMTS) mobile standard promulgated by theThird Generation Partnership Project (3GPP).

A wireless network may include one or more base stations that supportcommunication for a user equipment (UE) or multiple UEs. A UE maycommunicate with a base station via downlink communications and uplinkcommunications. “Downlink” (or “DL”) refers to a communication link fromthe base station to the UE, and “uplink” (or “UL”) refers to acommunication link from the UE to the base station.

The above multiple access technologies have been adopted in varioustelecommunication standards to provide a common protocol that enablesdifferent UEs to communicate on a municipal, national, regional, and/orglobal level. New Radio (NR), which may be referred to as 5G, is a setof enhancements to the LTE mobile standard promulgated by the 3GPP. NRis designed to better support mobile broadband internet access byimproving spectral efficiency, lowering costs, improving services,making use of new spectrum, and better integrating with other openstandards using orthogonal frequency division multiplexing (OFDM) with acyclic prefix (CP) (CP-OFDM) on the downlink, using CP-OFDM and/orsingle-carrier frequency division multiplexing (SC-FDM) (also known asdiscrete Fourier transform spread OFDM (DFT-s-OFDM)) on the uplink, aswell as supporting beamforming, multiple-input multiple-output (MIMO)antenna technology, and carrier aggregation. As the demand for mobilebroadband access continues to increase, further improvements in LTE, NR,and other radio access technologies remain useful.

SUMMARY

Some aspects described herein relate to a method of wirelesscommunication performed by a user equipment (UE). The method may includetransmitting capability information that indicates support for one ormore model combinations of machine learning (ML) models, wherein thecapability information further indicates one or more performanceparameters of an ML model of the ML models with respect to a modelcombination of the one or more model combinations that includes the MLmodel. The method may include receiving one or more indications to useone or more of the ML models based at least in part on the capabilityinformation.

Some aspects described herein relate to a method of wirelesscommunication performed by a network node. The method may includereceiving capability information that indicates support by a UE for oneor more model combinations of ML models, wherein the capabilityinformation further indicates one or more performance parameters of anML model of the one or more ML models with respect to a modelcombination of the one or more model combinations that includes the MLmodel. The method may include transmitting one or more indications touse one or more of the ML models based at least in part on thecapability information.

Some aspects described herein relate to a UE for wireless communication.The UE may include a memory and one or more processors coupled to thememory. The one or more processors may be configured to transmitcapability information that indicates support for one or more modelcombinations of ML models, wherein the capability information furtherindicates one or more performance parameters of an ML model of the MLmodels with respect to a model combination of the one or more modelcombinations that includes the ML model. The one or more processors maybe configured to receive one or more indications to use one or more ofthe ML models based at least in part on the capability information.

Some aspects described herein relate to a network node for wirelesscommunication. The network node may include a memory and one or moreprocessors coupled to the memory. The one or more processors may beconfigured to receive capability information that indicates support by aUE for one or more model combinations of ML models, wherein thecapability information further indicates one or more performanceparameters of an ML model of the one or more ML models with respect to amodel combination of the one or more model combinations that includesthe ML model. The one or more processors may be configured to transmitone or more indications to use one or more of the ML models based atleast in part on the capability information.

Some aspects described herein relate to a non-transitorycomputer-readable medium that stores a set of instructions for wirelesscommunication by a UE. The set of instructions, when executed by one ormore processors of the UE, may cause the UE to transmit capabilityinformation that indicates support for one or more model combinations ofML models, wherein the capability information further indicates one ormore performance parameters of an ML model of the ML models with respectto a model combination of the one or more model combinations thatincludes the ML model. The set of instructions, when executed by one ormore processors of the UE, may cause the UE to receive one or moreindications to use one or more of the ML models based at least in parton the capability information.

Some aspects described herein relate to a non-transitorycomputer-readable medium that stores a set of instructions for wirelesscommunication by a network node. The set of instructions, when executedby one or more processors of the network node, may cause the networknode to receive capability information that indicates support by a UEfor one or more model combinations of ML models, wherein the capabilityinformation further indicates one or more performance parameters of anML model of the one or more ML models with respect to a modelcombination of the one or more model combinations that includes the MLmodel. The set of instructions, when executed by one or more processorsof the network node, may cause the network node to transmit one or moreindications to use one or more of the ML models based at least in parton the capability information.

Some aspects described herein relate to an apparatus for wirelesscommunication. The apparatus may include means for transmittingcapability information that indicates support for one or more modelcombinations of ML models, wherein the capability information furtherindicates one or more performance parameters of an ML model of the MLmodels with respect to a model combination of the one or more modelcombinations that includes the ML model. The apparatus may include meansfor receiving one or more indications to use one or more of the MLmodels based at least in part on the capability information.

Some aspects described herein relate to an apparatus for wirelesscommunication. The apparatus may include means for receiving capabilityinformation that indicates support by a UE for one or more modelcombinations of ML models, wherein the capability information furtherindicates one or more performance parameters of an ML model of the oneor more ML models with respect to a model combination of the one or moremodel combinations that includes the ML model. The apparatus may includemeans for transmitting one or more indications to use one or more of theML models based at least in part on the capability information.

Aspects generally include a method, apparatus, system, computer programproduct, non-transitory computer-readable medium, user equipment, basestation, wireless communication device, and/or processing system assubstantially described herein with reference to and as illustrated bythe drawings and specification

The foregoing has outlined rather broadly the features and technicaladvantages of examples according to the disclosure in order that thedetailed description that follows may be better understood. Additionalfeatures and advantages will be described hereinafter. The conceptionand specific examples disclosed may be readily utilized as a basis formodifying or designing other structures for carrying out the samepurposes of the present disclosure. Such equivalent constructions do notdepart from the scope of the appended claims. Characteristics of theconcepts disclosed herein, both their organization and method ofoperation, together with associated advantages, will be betterunderstood from the following description when considered in connectionwith the accompanying figures. Each of the figures is provided for thepurposes of illustration and description, and not as a definition of thelimits of the claims.

While aspects are described in the present disclosure by illustration tosome examples, those skilled in the art will understand that suchaspects may be implemented in many different arrangements and scenarios.Techniques described herein may be implemented using different platformtypes, devices, systems, shapes, sizes, and/or packaging arrangements.For example, some aspects may be implemented via integrated chipembodiments or other non-module-component based devices (e.g., end-userdevices, vehicles, communication devices, computing devices, industrialequipment, retail/purchasing devices, medical devices, and/or artificialintelligence (AI) devices). Aspects may be implemented in chip-levelcomponents, modular components, non-modular components, non-chip-levelcomponents, device-level components, and/or system-level components.Devices incorporating described aspects and features may includeadditional components and features for implementation and practice ofclaimed and described aspects. For example, transmission and receptionof wireless signals may include one or more components for analog anddigital purposes (e.g., hardware components including antennas, radiofrequency (RF) chains, power amplifiers, modulators, buffers,processors, interleavers, adders, and/or summers). It is intended thataspects described herein may be practiced in a wide variety of devices,components, systems, distributed arrangements, and/or end-user devicesof varying size, shape, and constitution.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the above-recited features of the present disclosure can beunderstood in detail, a more particular description, briefly summarizedabove, may be had by reference to aspects, some of which are illustratedin the appended drawings. It is to be noted, however, that the appendeddrawings illustrate only certain typical aspects of this disclosure andare therefore not to be considered limiting of its scope, for thedescription may admit to other equally effective aspects. The samereference numbers in different drawings may identify the same or similarelements.

FIG. 1 is a diagram illustrating an example of a wireless network, inaccordance with the present disclosure.

FIG. 2 is a diagram illustrating an example of a base station incommunication with a user equipment (UE) in a wireless network, inaccordance with the present disclosure.

FIG. 3 is a diagram illustrating an example 300 disaggregated basestation architecture, in accordance with the present disclosure.

FIG. 4 is a diagram illustrating an example associated withcommunicating UE capabilities for machine learning models, in accordancewith the present disclosure.

FIGS. 5-7 are diagrams illustrating examples associated with parametersfor combinations of machine learning models, in accordance with thepresent disclosure.

FIGS. 8 and 9 are diagrams illustrating example processes associatedwith parameters for combinations of machine learning models, inaccordance with the present disclosure.

FIGS. 10 and 11 are diagrams of example apparatuses for wirelesscommunication, in accordance with the present disclosure.

DETAILED DESCRIPTION

Various aspects of the disclosure are described more fully hereinafterwith reference to the accompanying drawings. This disclosure may,however, be embodied in many different forms and should not be construedas limited to any specific structure or function presented throughoutthis disclosure. Rather, these aspects are provided so that thisdisclosure will be thorough and complete, and will fully convey thescope of the disclosure to those skilled in the art. One skilled in theart should appreciate that the scope of the disclosure is intended tocover any aspect of the disclosure disclosed herein, whether implementedindependently of or combined with any other aspect of the disclosure.For example, an apparatus may be implemented or a method may bepracticed using any number of the aspects set forth herein. In addition,the scope of the disclosure is intended to cover such an apparatus ormethod which is practiced using other structure, functionality, orstructure and functionality in addition to or other than the variousaspects of the disclosure set forth herein. It should be understood thatany aspect of the disclosure disclosed herein may be embodied by one ormore elements of a claim.

Several aspects of telecommunication systems will now be presented withreference to various apparatuses and techniques. These apparatuses andtechniques will be described in the following detailed description andillustrated in the accompanying drawings by various blocks, modules,components, circuits, steps, processes, algorithms, or the like(collectively referred to as “elements”). These elements may beimplemented using hardware, software, or combinations thereof. Whethersuch elements are implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem.

While aspects may be described herein using terminology commonlyassociated with a 5G or New Radio (NR) radio access technology (RAT),aspects of the present disclosure can be applied to other RATs, such asa 3G RAT, a 4G RAT, and/or a RAT subsequent to 5G (e.g., 6G).

FIG. 1 is a diagram illustrating an example of a wireless network 100,in accordance with the present disclosure. The wireless network 100 maybe or may include elements of a 5G (e.g., NR) network and/or a 4G (e.g.,Long Term Evolution (LTE)) network, among other examples. The wirelessnetwork 100 may include one or more base stations 110 (shown as a BS 110a, a BS 110 b, a BS 110 c, and a BS 110 d), a user equipment (UE) 120 ormultiple UEs 120 (shown as a UE 120 a, a UE 120 b, a UE 120 c, a UE 120d, and a UE 120 e), and/or other network entities. A base station 110 isan entity that communicates with UEs 120. A base station 110 (sometimesreferred to as a BS) may include, for example, an NR base station, anLTE base station, a Node B, an eNB (e.g., in 4G), a gNB (e.g., in 5G),an access point, and/or a transmission reception point (TRP). Each basestation 110 may provide communication coverage for a particulargeographic area. In the Third Generation Partnership Project (3GPP), theterm “cell” can refer to a coverage area of a base station 110 and/or abase station subsystem serving this coverage area, depending on thecontext in which the term is used.

A base station 110 may provide communication coverage for a macro cell,a pico cell, a femto cell, and/or another type of cell. A macro cell maycover a relatively large geographic area (e.g., several kilometers inradius) and may allow unrestricted access by UEs 120 with servicesubscriptions. A pico cell may cover a relatively small geographic areaand may allow unrestricted access by UEs 120 with service subscription.A femto cell may cover a relatively small geographic area (e.g., a home)and may allow restricted access by UEs 120 having association with thefemto cell (e.g., UEs 120 in a closed subscriber group (CSG)). A basestation 110 for a macro cell may be referred to as a macro base station.A base station 110 for a pico cell may be referred to as a pico basestation. A base station 110 for a femto cell may be referred to as afemto base station or an in-home base station. In the example shown inFIG. 1 , the BS 110 a may be a macro base station for a macro cell 102a, the BS 110 b may be a pico base station for a pico cell 102 b, andthe BS 110 c may be a femto base station for a femto cell 102 c. A basestation may support one or multiple (e.g., three) cells.

In some examples, a cell may not necessarily be stationary, and thegeographic area of the cell may move according to the location of a basestation 110 that is mobile (e.g., a mobile base station). In someexamples, the base stations 110 may be interconnected to one anotherand/or to one or more other base stations 110 or network nodes (notshown) in the wireless network 100 through various types of backhaulinterfaces, such as a direct physical connection or a virtual network,using any suitable transport network.

The wireless network 100 may include one or more relay stations. A relaystation is an entity that can receive a transmission of data from anupstream station (e.g., a base station 110 or a UE 120) and send atransmission of the data to a downstream station (e.g., a UE 120 or abase station 110). A relay station may be a UE 120 that can relaytransmissions for other UEs 120. In the example shown in FIG. 1 , the BS110 d (e.g., a relay base station) may communicate with the BS 110 a(e.g., a macro base station) and the UE 120 d in order to facilitatecommunication between the BS 110 a and the UE 120 d. A base station 110that relays communications may be referred to as a relay station, arelay base station, a relay, or the like.

The wireless network 100 may be a heterogeneous network that includesbase stations 110 of different types, such as macro base stations, picobase stations, femto base stations, relay base stations, or the like.These different types of base stations 110 may have different transmitpower levels, different coverage areas, and/or different impacts oninterference in the wireless network 100. For example, macro basestations may have a high transmit power level (e.g., 5 to 40 watts)whereas pico base stations, femto base stations, and relay base stationsmay have lower transmit power levels (e.g., 0.1 to 2 watts).

A network controller 130 may couple to or communicate with a set of basestations 110 and may provide coordination and control for these basestations 110. The network controller 130 may communicate with the basestations 110 via a backhaul communication link. The base stations 110may communicate with one another directly or indirectly via a wirelessor wireline backhaul communication link.

The UEs 120 may be dispersed throughout the wireless network 100, andeach UE 120 may be stationary or mobile. A UE 120 may include, forexample, an access terminal, a terminal, a mobile station, and/or asubscriber unit. A UE 120 may be a cellular phone (e.g., a smart phone),a personal digital assistant (PDA), a wireless modem, a wirelesscommunication device, a handheld device, a laptop computer, a cordlessphone, a wireless local loop (WLL) station, a tablet, a camera, a gamingdevice, a netbook, a smartbook, an ultrabook, a medical device, abiometric device, a wearable device (e.g., a smart watch, smartclothing, smart glasses, a smart wristband, smart jewelry (e.g., a smartring or a smart bracelet)), an entertainment device (e.g., a musicdevice, a video device, and/or a satellite radio), a vehicular componentor sensor, a smart meter/sensor, industrial manufacturing equipment, aglobal positioning system device, and/or any other suitable device thatis configured to communicate via a wireless medium.

Some UEs 120 may be considered machine-type communication (MTC) orevolved or enhanced machine-type communication (eMTC) UEs. An MTC UEand/or an eMTC UE may include, for example, a robot, a drone, a remotedevice, a sensor, a meter, a monitor, and/or a location tag, that maycommunicate with a base station, another device (e.g., a remote device),or some other entity. Some UEs 120 may be considered Internet-of-Things(IoT) devices, and/or may be implemented as NB-IoT (narrowband IoT)devices. Some UEs 120 may be considered a Customer Premises Equipment. AUE 120 may be included inside a housing that houses components of the UE120, such as processor components and/or memory components. In someexamples, the processor components and the memory components may becoupled together. For example, the processor components (e.g., one ormore processors) and the memory components (e.g., a memory) may beoperatively coupled, communicatively coupled, electronically coupled,and/or electrically coupled.

In general, any number of wireless networks 100 may be deployed in agiven geographic area. Each wireless network 100 may support aparticular RAT and may operate on one or more frequencies. A RAT may bereferred to as a radio technology, an air interface, or the like. Afrequency may be referred to as a carrier, a frequency channel, or thelike. Each frequency may support a single RAT in a given geographic areain order to avoid interference between wireless networks of differentRATs. In some cases, NR or 5G RAT networks may be deployed.

In some examples, two or more UEs 120 (e.g., shown as UE 120 a and UE120 e) may communicate directly using one or more sidelink channels(e.g., without using a base station 110 as an intermediary tocommunicate with one another). For example, the UEs 120 may communicateusing peer-to-peer (P2P) communications, device-to-device (D2D)communications, a vehicle-to-everything (V2X) protocol (e.g., which mayinclude a vehicle-to-vehicle (V2V) protocol, a vehicle-to-infrastructure(V2I) protocol, or a vehicle-to-pedestrian (V2P) protocol), and/or amesh network. In such examples, a UE 120 may perform schedulingoperations, resource selection operations, and/or other operationsdescribed elsewhere herein as being performed by the base station 110.

Devices of the wireless network 100 may communicate using theelectromagnetic spectrum, which may be subdivided by frequency orwavelength into various classes, bands, channels, or the like. Forexample, devices of the wireless network 100 may communicate using oneor more operating bands. In 5G NR, two initial operating bands have beenidentified as frequency range designations FR1 (410 MHz-7.125 GHz) andFR2 (24.25 GHz-52.6 GHz). It should be understood that although aportion of FR1 is greater than 6 GHz, FR1 is often referred to(interchangeably) as a “Sub-6 GHz” band in various documents andarticles. A similar nomenclature issue sometimes occurs with regard toFR2, which is often referred to (interchangeably) as a “millimeter wave”band in documents and articles, despite being different from theextremely high frequency (EHF) band (30 GHz-300 GHz) which is identifiedby the International Telecommunications Union (ITU) as a “millimeterwave” band.

The frequencies between FR1 and FR2 are often referred to as mid-bandfrequencies. Recent 5G NR studies have identified an operating band forthese mid-band frequencies as frequency range designation FR3 (7.125GHz-24.25 GHz). Frequency bands falling within FR3 may inherit FR1characteristics and/or FR2 characteristics, and thus may effectivelyextend features of FR1 and/or FR2 into mid-band frequencies. Inaddition, higher frequency bands are currently being explored to extend5G NR operation beyond 52.6 GHz. For example, three higher operatingbands have been identified as frequency range designations FR4a or FR4-1(52.6 GHz-71 GHz), FR4 (52.6 GHz-114.25 GHz), and FR5 (114.25 GHz-300GHz). Each of these higher frequency bands falls within the EHF band.

With the above examples in mind, unless specifically stated otherwise,it should be understood that the term “sub-6 GHz” or the like, if usedherein, may broadly represent frequencies that may be less than 6 GHz,may be within FR1, or may include mid-band frequencies. Further, unlessspecifically stated otherwise, it should be understood that the term“millimeter wave” or the like, if used herein, may broadly representfrequencies that may include mid-band frequencies, may be within FR2,FR4, FR4-a or FR4-1, and/or FR5, or may be within the EHF band. It iscontemplated that the frequencies included in these operating bands(e.g., FR1, FR2, FR3, FR4, FR4-a, FR4-1, and/or FR5) may be modified,and techniques described herein are applicable to those modifiedfrequency ranges.

In some aspects, the UE 120 may include a communication manager 140. Asdescribed in more detail elsewhere herein, the communication manager 140may transmit capability information that indicates support for one ormore model combinations of ML models, wherein the capability informationfurther indicates one or more performance parameters of an ML model ofthe ML models with respect to a model combination of the one or moremodel combinations that includes the ML model; and receive one or moreindications to use one or more of the ML models based at least in parton the capability information. Additionally, or alternatively, thecommunication manager 140 may perform one or more other operationsdescribed herein.

In some aspects, a network node (e.g., the base station 110) may includea communication manager 150. As described in more detail elsewhereherein, the communication manager 150 may receive capability informationthat indicates support by a UE for one or more model combinations of MLmodels, wherein the capability information further indicates one or moreperformance parameters of an ML model of the one or more ML models withrespect to a model combination of the one or more model combinationsthat includes the ML model; and transmit one or more indications to useone or more of the ML models based at least in part on the capabilityinformation. Additionally, or alternatively, the communication manager150 may perform one or more other operations described herein.

In some aspects, the term “base station” (e.g., the base station 110) or“network node” or “network entity” may refer to an aggregated basestation, a disaggregated base station (e.g., described in connectionwith FIG. 9 ), an integrated access and backhaul (IAB) node, a relaynode, and/or one or more components thereof. For example, in someaspects, “base station,” “network node,” or “network entity” may referto a central unit (CU or centralized unit), a distributed unit (DU), aradio unit (RU), a Near-Real Time (Near-RT) RAN Intelligent Controller(RIC), or a Non-Real Time (Non-RT) RIC, or a combination thereof. Insome aspects, the term “base station,” “network node,” or “networkentity” may refer to one device configured to perform one or morefunctions, such as those described herein in connection with the basestation 110. In some aspects, the term “base station,” “network node,”or “network entity” may refer to a plurality of devices configured toperform the one or more functions. For example, in some distributedsystems, each of a number of different devices (which may be located inthe same geographic location or in different geographic locations) maybe configured to perform at least a portion of a function, or toduplicate performance of at least a portion of the function, and theterm “base station,” “network node,” or “network entity” may refer toany one or more of those different devices. In some aspects, the term“base station,” “network node,” or “network entity” may refer to one ormore virtual base stations and/or one or more virtual base stationfunctions. For example, in some aspects, two or more base stationfunctions may be instantiated on a single device. In some aspects, theterm “base station,” “network node,” or “network entity” may refer toone of the base station functions and not another. In this way, a singledevice may include more than one base station.

As indicated above, FIG. 1 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 1 .

FIG. 2 is a diagram illustrating an example 200 of a base station 110 incommunication with a UE 120 in a wireless network 100, in accordancewith the present disclosure. The base station 110 may be equipped with aset of antennas 234 a through 234 t, such as T antennas (T≥1). The UE120 may be equipped with a set of antennas 252 a through 252 r, such asR antennas (R≥1).

At the base station 110, a transmit processor 220 may receive data, froma data source 212, intended for the UE 120 (or a set of UEs 120). Thetransmit processor 220 may select one or more modulation and codingschemes (MCSs) for the UE 120 based at least in part on one or morechannel quality indicators (CQIs) received from that UE 120. The basestation 110 may process (e.g., encode and modulate) the data for the UE120 based at least in part on the MCS(s) selected for the UE 120 and mayprovide data symbols for the UE 120. The transmit processor 220 mayprocess system information (e.g., for semi-static resource partitioninginformation (SRPI)) and control information (e.g., CQI requests, grants,and/or upper layer signaling) and provide overhead symbols and controlsymbols. The transmit processor 220 may generate reference symbols forreference signals (e.g., a cell-specific reference signal (CRS) or ademodulation reference signal (DMRS)) and synchronization signals (e.g.,a primary synchronization signal (PSS) or a secondary synchronizationsignal (SSS)). A transmit (TX) multiple-input multiple-output (MIMO)processor 230 may perform spatial processing (e.g., precoding) on thedata symbols, the control symbols, the overhead symbols, and/or thereference symbols, if applicable, and may provide a set of output symbolstreams (e.g., T output symbol streams) to a corresponding set of modems232 (e.g., T modems), shown as modems 232 a through 232 t. For example,each output symbol stream may be provided to a modulator component(shown as MOD) of a modem 232. Each modem 232 may use a respectivemodulator component to process a respective output symbol stream (e.g.,for OFDM) to obtain an output sample stream. Each modem 232 may furtheruse a respective modulator component to process (e.g., convert toanalog, amplify, filter, and/or upconvert) the output sample stream toobtain a downlink signal. The modems 232 a through 232 t may transmit aset of downlink signals (e.g., T downlink signals) via a correspondingset of antennas 234 (e.g., T antennas), shown as antennas 234 a through234 t.

At the UE 120, a set of antennas 252 (shown as antennas 252 a through252 r) may receive the downlink signals from the base station 110 and/orother base stations 110 and may provide a set of received signals (e.g.,R received signals) to a set of modems 254 (e.g., R modems), shown asmodems 254 a through 254 r. For example, each received signal may beprovided to a demodulator component (shown as DEMOD) of a modem 254.Each modem 254 may use a respective demodulator component to condition(e.g., filter, amplify, downconvert, and/or digitize) a received signalto obtain input samples. Each modem 254 may use a demodulator componentto further process the input samples (e.g., for OFDM) to obtain receivedsymbols. A MIMO detector 256 may obtain received symbols from the modems254, may perform MIMO detection on the received symbols if applicable,and may provide detected symbols. A receive processor 258 may process(e.g., demodulate and decode) the detected symbols, may provide decodeddata for the UE 120 to a data sink 260, and may provide decoded controlinformation and system information to a controller/processor 280. Theterm “controller/processor” may refer to one or more controllers, one ormore processors, or a combination thereof. A channel processor maydetermine a reference signal received power (RSRP) parameter, a receivedsignal strength indicator (RSSI) parameter, a reference signal receivedquality (RSRQ) parameter, and/or a CQI parameter, among other examples.In some examples, one or more components of the UE 120 may be includedin a housing 284.

The network controller 130 may include a communication unit 294, acontroller/processor 290, and a memory 292. The network controller 130may include, for example, one or more devices in a core network. Thenetwork controller 130 may communicate with the base station 110 via thecommunication unit 294.

One or more antennas (e.g., antennas 234 a through 234 t and/or antennas252 a through 252 r) may include, or may be included within, one or moreantenna panels, one or more antenna groups, one or more sets of antennaelements, and/or one or more antenna arrays, among other examples. Anantenna panel, an antenna group, a set of antenna elements, and/or anantenna array may include one or more antenna elements (within a singlehousing or multiple housings), a set of coplanar antenna elements, a setof non-coplanar antenna elements, and/or one or more antenna elementscoupled to one or more transmission and/or reception components, such asone or more components of FIG. 2 .

On the uplink, at the UE 120, a transmit processor 264 may receive andprocess data from a data source 262 and control information (e.g., forreports that include RSRP, RSSI, RSRQ, and/or CQI) from thecontroller/processor 280. The transmit processor 264 may generatereference symbols for one or more reference signals. The symbols fromthe transmit processor 264 may be precoded by a TX MIMO processor 266 ifapplicable, further processed by the modems 254 (e.g., for DFT-s-OFDM orCP-OFDM), and transmitted to the base station 110. In some examples, themodem 254 of the UE 120 may include a modulator and a demodulator. Insome examples, the UE 120 includes a transceiver. The transceiver mayinclude any combination of the antenna(s) 252, the modem(s) 254, theMIMO detector 256, the receive processor 258, the transmit processor264, and/or the TX MIMO processor 266. The transceiver may be used by aprocessor (e.g., the controller/processor 280) and the memory 282 toperform aspects of any of the methods described herein (e.g., withreference to FIGS. 5-11 ).

At the base station 110, the uplink signals from UE 120 and/or other UEsmay be received by the antennas 234, processed by the modem 232 (e.g., ademodulator component, shown as DEMOD, of the modem 232), detected by aMIMO detector 236 if applicable, and further processed by a receiveprocessor 238 to obtain decoded data and control information sent by theUE 120. The receive processor 238 may provide the decoded data to a datasink 239 and provide the decoded control information to thecontroller/processor 240. The base station 110 may include acommunication unit 244 and may communicate with the network controller130 via the communication unit 244. The base station 110 may include ascheduler 246 to schedule one or more UEs 120 for downlink and/or uplinkcommunications. In some examples, the modem 232 of the base station 110may include a modulator and a demodulator. In some examples, the basestation 110 includes a transceiver. The transceiver may include anycombination of the antenna(s) 234, the modem(s) 232, the MIMO detector236, the receive processor 238, the transmit processor 220, and/or theTX MIMO processor 230. The transceiver may be used by a processor (e.g.,the controller/processor 240) and the memory 242 to perform aspects ofany of the methods described herein (e.g., with reference to FIGS. 5-11).

The controller/processor 240 of the base station 110, thecontroller/processor 280 of the UE 120, and/or any other component(s) ofFIG. 2 may perform one or more techniques associated with parameters forcombinations of ML models, as described in more detail elsewhere herein.In some aspects, the network node described herein is the base station110, is included in the base station 110, or includes one or morecomponents of the base station 110 shown in FIG. 2 . For example, thecontroller/processor 240 of the base station 110, thecontroller/processor 280 of the UE 120, and/or any other component(s) ofFIG. 2 may perform or direct operations of, for example, process 800 ofFIG. 8 , process 900 of FIG. 9 , and/or other processes as describedherein. The memory 242 and the memory 282 may store data and programcodes for the base station 110 and the UE 120, respectively. In someexamples, the memory 242 and/or the memory 282 may include anon-transitory computer-readable medium storing one or more instructions(e.g., code and/or program code) for wireless communication. Forexample, the one or more instructions, when executed (e.g., directly, orafter compiling, converting, and/or interpreting) by one or moreprocessors of the base station 110 and/or the UE 120, may cause the oneor more processors, the UE 120, and/or the base station 110 to performor direct operations of, for example, process 800 of FIG. 8 , process900 of FIG. 9 , and/or other processes as described herein. In someexamples, executing instructions may include running the instructions,converting the instructions, compiling the instructions, and/orinterpreting the instructions, among other examples.

In some aspects, a UE (e.g., the UE 120) includes means for transmittingcapability information that indicates support for one or more modelcombinations of ML models, wherein the capability information furtherindicates one or more performance parameters of an ML model of the MLmodels with respect to a model combination of the one or more modelcombinations that includes the ML model; and/or means for receiving oneor more indications to use one or more of the ML models based at leastin part on the capability information. The means for the UE to performoperations described herein may include, for example, one or more ofcommunication manager 140, antenna 252, modem 254, MIMO detector 256,receive processor 258, transmit processor 264, TX MIMO processor 266,controller/processor 280, or memory 282.

In some aspects, a network node (e.g., the base station 110) includesmeans for receiving capability information that indicates support by aUE for one or more model combinations of ML models, wherein thecapability information further indicates one or more performanceparameters of an ML model of the one or more ML models with respect to amodel combination of the one or more model combinations that includesthe ML model; and/or means for transmitting one or more indications touse one or more of the ML models based at least in part on thecapability information. In some aspects, the means for the network nodeto perform operations described herein may include, for example, one ormore of communication manager 150, transmit processor 220, TX MIMOprocessor 230, modem 232, antenna 234, MIMO detector 236, receiveprocessor 238, controller/processor 240, memory 242, or scheduler 246.

While blocks in FIG. 2 are illustrated as distinct components, thefunctions described above with respect to the blocks may be implementedin a single hardware, software, or combination component or in variouscombinations of components. For example, the functions described withrespect to the transmit processor 264, the receive processor 258, and/orthe TX MIMO processor 266 may be performed by or under the control ofthe controller/processor 280.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 2 .

FIG. 3 is a diagram illustrating an example 300 disaggregated basestation architecture, in accordance with the present disclosure.

Deployment of communication systems, such as 5G NR systems, may bearranged in multiple manners with various components or constituentparts. In a 5G NR system, or network, a network node, a network entity,a mobility element of a network, a RAN node, a core network node, anetwork element, or a network equipment, such as a base station (BS,e.g., base station 110), or one or more units (or one or morecomponents) performing base station functionality, may be implemented inan aggregated or disaggregated architecture. For example, a BS (such asa Node B (NB), eNB, NR BS, 5G NB, access point (AP), a TRP, a cell, orthe like) may be implemented as an aggregated base station (also knownas a standalone BS or a monolithic BS) or a disaggregated base station.

An aggregated base station may be configured to utilize a radio protocolstack that is physically or logically integrated within a single RANnode. A disaggregated base station may be configured to utilize aprotocol stack that is physically or logically distributed among two ormore units (such as one or more CUs, one or more DUs, or one or moreRUs). In some aspects, a CU may be implemented within a RAN node, andone or more DUs may be co-located with the CU, or alternatively, may begeographically or virtually distributed throughout one or multiple otherRAN nodes. The DUs may be implemented to communicate with one or moreRUs. Each of the CU, DU and RU also can be implemented as virtual units,i.e., a virtual centralized unit (VCU), a virtual distributed unit(VDU), or a virtual radio unit (VRU).

Base station-type operation or network design may consider aggregationcharacteristics of base station functionality. For example,disaggregated base stations may be utilized in an integrated accessbackhaul (IAB) network, an O-RAN (such as the network configurationsponsored by the O-RAN Alliance), or a virtualized radio access network(vRAN, also known as a cloud radio access network (C-RAN)).Disaggregation may include distributing functionality across two or moreunits at various physical locations, as well as distributingfunctionality for at least one unit virtually, which can enableflexibility in network design. The various units of the disaggregatedbase station, or disaggregated RAN architecture, can be configured forwired or wireless communication with at least one other unit.

The disaggregated base station architecture shown in FIG. 3 may includeone or more CUs 310 that can communicate directly with a core network320 via a backhaul link, or indirectly with the core network 320 throughone or more disaggregated base station units (such as a Near-RT RAN RIC325 via an E2 link, or a Non-RT RIC 315 associated with a ServiceManagement and Orchestration (SMO) Framework 305, or both). A CU 310 maycommunicate with one or more DUs 330 via respective midhaul links, suchas an F1 interface. The DUs 330 may communicate with one or more RUs 340via respective fronthaul links. The RUs 340 may communicate withrespective UEs 120 via one or more radio frequency (RF) access links. Insome implementations, the UE 120 may be simultaneously served bymultiple RUs 340.

Each of the units (e.g., the CUs 310, the DUs 330, the RUs 340), as wellas the Near-RT RICs 325, the Non-RT RICs 315, and the SMO Framework 305,may include one or more interfaces or be coupled to one or moreinterfaces configured to receive or transmit signals, data, orinformation (collectively, signals) via a wired or wireless transmissionmedium. Each of the units, or an associated processor or controllerproviding instructions to the communication interfaces of the units, canbe configured to communicate with one or more of the other units via thetransmission medium. For example, the units can include a wiredinterface configured to receive or transmit signals over a wiredtransmission medium to one or more of the other units. Additionally, theunits can include a wireless interface, which may include a receiver, atransmitter or transceiver (such as an RF transceiver), configured toreceive or transmit signals, or both, over a wireless transmissionmedium to one or more of the other units.

In some aspects, the CU 310 may host one or more higher layer controlfunctions. Such control functions can include radio resource control(RRC), packet data convergence protocol (PDCP), service data adaptationprotocol (SDAP), or the like. Each control function can be implementedwith an interface configured to communicate signals with other controlfunctions hosted by the CU 310. The CU 310 may be configured to handleuser plane functionality (e.g., Central Unit-User Plane (CU-UP)),control plane functionality (e.g., Central Unit-Control Plane (CU-CP)),or a combination thereof. In some implementations, the CU 310 can belogically split into one or more CU-UP units and one or more CU-CPunits. The CU-UP unit can communicate bidirectionally with the CU-CPunit via an interface, such as the E1 interface when implemented in anO-RAN configuration. The CU 310 can be implemented to communicate withthe DU 330, as necessary, for network control and signaling.

The DU 330 may correspond to a logical unit that includes one or morebase station functions to control the operation of one or more RUs 340.In some aspects, the DU 330 may host one or more of a radio link control(RLC) layer, a medium access control (MAC) layer, and one or more highphysical (PHY) layers (such as modules for forward error correction(FEC) encoding and decoding, scrambling, modulation and demodulation, orthe like) depending, at least in part, on a functional split, such asthose defined by the 3rd Generation Partnership Project (3GPP). In someaspects, the DU 330 may further host one or more low-PHY layers. Eachlayer (or module) can be implemented with an interface configured tocommunicate signals with other layers (and modules) hosted by the DU330, or with the control functions hosted by the CU 310.

Lower-layer functionality can be implemented by one or more RUs 340. Insome deployments, an RU 340, controlled by a DU 330, may correspond to alogical node that hosts RF processing functions, or low-PHY layerfunctions (such as performing fast Fourier transform (FFT), inverse FFT(iFFT), digital beamforming, physical random access channel (PRACH)extraction and filtering, or the like), or both, based at least in parton the functional split, such as a lower layer functional split. In suchan architecture, the RU(s) 340 can be implemented to handle over the air(OTA) communication with one or more UEs 120. In some implementations,real-time and non-real-time aspects of control and user planecommunication with the RU(s) 340 can be controlled by the correspondingDU 330. In some scenarios, this configuration can enable the DU(s) 330and the CU 310 to be implemented in a cloud-based RAN architecture, suchas a vRAN architecture.

The SMO Framework 305 may be configured to support RAN deployment andprovisioning of non-virtualized and virtualized network elements. Fornon-virtualized network elements, the SMO Framework 305 may beconfigured to support the deployment of dedicated physical resources forRAN coverage requirements which may be managed via an operations andmaintenance interface (such as an O1 interface). For virtualized networkelements, the SMO Framework 305 may be configured to interact with acloud computing platform (such as an open cloud (O-Cloud) 390) toperform network element life cycle management (such as to instantiatevirtualized network elements) via a cloud computing platform interface(such as an O2 interface). Such virtualized network elements caninclude, but are not limited to, CUs 310, DUs 330, RUs 340 and Near-RTRICs 325. In some implementations, the SMO Framework 305 can communicatewith a hardware aspect of a 4G RAN, such as an open eNB (O-eNB) 311, viaan O1 interface. Additionally, in some implementations, the SMOFramework 305 can communicate directly with one or more RUs 340 via anO1 interface. The SMO Framework 305 also may include a Non-RT RIC 315configured to support functionality of the SMO Framework 305.

The Non-RT RIC 315 may be configured to include a logical function thatenables non-real-time control and optimization of RAN elements andresources, Artificial Intelligence/Machine Learning (AI/ML) workflowsincluding model training and updates, or policy-based guidance ofapplications/features in the Near-RT RIC 325. The Non-RT RIC 315 may becoupled to or communicate with (such as via an A1 interface) the Near-RTRIC 325. The Near-RT RIC 325 may be configured to include a logicalfunction that enables near-real-time control and optimization of RANelements and resources via data collection and actions over an interface(such as via an E2 interface) connecting one or more CUs 310, one ormore DUs 330, or both, as well as an O-eNB, with the Near-RT RIC 325.

In some implementations, to generate AI/ML models to be deployed in theNear-RT RIC 325, the Non-RT RIC 315 may receive parameters or externalenrichment information from external servers. Such information may beutilized by the Near-RT RIC 325 and may be received at the SMO Framework305 or the Non-RT RIC 315 from non-network data sources or from networkfunctions. In some examples, the Non-RT RIC 315 or the Near-RT RIC 325may be configured to tune RAN behavior or performance. For example, theNon-RT RIC 315 may monitor long-term trends and patterns for performanceand employ AI/ML models to perform corrective actions through the SMOFramework 305 (such as reconfiguration via 01) or via creation of RANmanagement policies (such as A1 policies).

As indicated above, FIG. 3 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 3 .

FIG. 4 is a diagram illustrating an example 400 associated withcommunicating UE capabilities for ML models, in accordance with thepresent disclosure. As shown in FIG. 4 , a network node and a UE maycommunicate via a wireless network (e.g., wireless network 100). The UEmay support one or more ML models associated with communicating with thenetwork node.

As shown by reference number 405, the UE may receive, and the networknode may transmit, a UE capability inquiry. For example, the networknode may request an indication of supported ML models and/or performanceof each of the ML models.

As shown by reference number 410, the UE may transmit, and the networknode may receive, a capability report associated with individual MLmodels. For example, the capability report may indicate each supportedML model and may indicate performance parameters for each of thesupported ML models.

As shown by reference number 415, the UE may receive, and the networknode may transmit, an indication of a set of one or more ML models touse in communication with the network node. The ML models may include MLmodels that the UE may perform to improve communication efficiency whencommunicating via the network node.

The UE may indicate values of performance parameters for each of the MLmodels when used individually. However, a value associated with a firstML model may change based at least in part on other ML models used incombination with the first ML model.

ML models used in a combination of ML models may compete for resourcesof the UE, such as central processing unit (CPU) resources, neuralprocessing unit (NPU) resources, graphics processing unit (GPU)resources, memory resources, and/or input/output (I/O) resources of theUE, among other examples. For example, ML models may share and/orcontend for special resources other than general resources, such as aspecial hardware acceleration module like a fast Fourier transform (FFT)module, among other examples.

Additionally, or alternatively, multiple ML models used in combinationmay target the same or related network functions, such as a first MLmodel that controls radio resource management (RRM) measurement and asecond ML model that controls cell reselection. In this case, the UE mayconsume resources for the first ML model and the second ML model, whichmay be unnecessary and/or duplicative, and may consume resources thatmay have otherwise been used in applying another ML model. Further,multiple ML models used in combination may have dependency, such as afirst ML model with output that is used as input for a second ML model.

In some examples, a first ML model may include a CSI reporting ML modelfor determining a set of subbands to use for reporting. Accurateselection of set of the subbands using the first ML model may improvecommunication efficiency and/or reduce overhead. However, a second MLmodel consumes resources needed by the first ML model (e.g., computingresources and/or memory resources, among other examples), the UE mayfail to identify the set of subbands before the CSI reporting becomesoutdated and/or obsolete.

In these cases, among other examples, reported values of the performanceparameters for each of the ML models when used individually may notrepresent performance parameters of the ML models when used in a modelcombination of ML models. For example, performance of the ML models whenused in a model combination may be reduced and/or diminished to a pointwhere use of the ML models reduces communication efficiency and/orconsumes unnecessary power, communication, network, and/or computingresources, based at least in part on, for example, a latency ingenerating outputs of the ML models.

As indicated above, FIG. 4 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 4 .

In some aspects described herein, a UE may indicate performanceparameters for ML models when used in different model combinations. Forexample, the UE may transmit capability information that indicatessupport for one or more model combinations of ML models, with thecapability information indicating one or more performance parameters ofan ML model of the ML models with respect to a model combination of theone or more model combinations that includes the ML model. In someaspects, mapping between a model identification and an indication of aperformance class is not fixed, and is based at least in part on acombination in which the associated ML model is used. In some aspects,the performance parameters may be based at least in part on the MLmodels competing for resources of the UE, such as CPU resources, NPUresources, GPU resources, memory resources, and/or I/O resources of theUE, among other examples.

In some aspects, the UE may indicate supported model combinations and/ormay indicate unsupported model combinations (e.g., in a same or separatecommunication as the indication of performance parameters).

The UE may transmit the capability information including a list of modelcombinations (e.g., indicating combinations of ML models that the UE cansupport). The capability information may also include information for afirst model combination that indicates a first ML model identificationand an associated indication of performance (e.g., a model performanceclass) of the first ML model when used in the first model combination,and a second ML model identification and an associated indication ofperformance of the second ML model when used in the first modelcombination. The information for the first model combination may includeindications of performance for any number of ML models supported by theUE for combination. The capability information may also includeinformation for a second model combination that indicates a third MLmodel identification (e.g., a same ML model identification as the firstor second ML model identification or an additional ML modelidentification) and an associated indication of performance of the thirdML model when used in the second model combination, and a fourth MLmodel identification (e.g., a same ML model identification as the firstor second ML model identification or an additional ML modelidentification) and an associated indication of performance of thefourth ML model when used in the first model combination. In someaspects, an ML model may have different indications of performance whenused in different model combinations.

In some aspects, the indication of performance may map to a set ofperformance metrics and/or values. For example, the indication ofperformance may include an indication of a performance class that mapsto values of performance parameters related to, for example, an AIengine, memory, quantization (e.g., granularity of model outputs),and/or delay in generating outputs. The values of the performanceparameters may be explicit values and/or may be relative to values ofthe performance parameters when the ML models are used independently(e.g., not in combination with other ML models). In some aspects, themapping may be defined in a table or other data storage structure. Themapping may be defined in a communication protocol, a communicationstandard, and/or via bilateral or multilateral coordination. Forexample, UE vendors and infrastructure vendors may share a sameunderstanding on a meaning of each performance class and/or otherindication of performance.

In some aspects, the UE may transmit the capability information in anRRC message or other message type that indicates capabilities. The UEmay report the capability information via part of a UE radio capability(e.g., received by a base station and forwarded to a core networknetwork node), a UE core network capability (e.g., received by a corenetwork network node and forwarded to a network node), and/or a UE MLcapability (e.g., a new indication or combined with the UE radiocapability or core network capability information). In some aspects,based at least in part on the UE reporting the capability informationassociated with model combinations to a first network node, the firstnetwork node may forward the capability information to a second networknode and/or additional network nodes.

Based at least in part on the UE indicating performance parameters of MLmodels when used in combination with other ML models, the UE may providethe network node with an indication of performance of the ML models withimproved accuracy. In this way, the network node may configure the UE touse a combination of ML models that is based at least in part on theimproved accuracy such that performance of the ML models may besufficient to improve communication efficiency and/or conserve power,communication, network, and/or computing resources that may haveotherwise been consumed to attempt to use additional ML models withfurther reduced performance.

FIG. 5 is a diagram of an example 500 associated with parameters forcombinations of ML models, in accordance with the present disclosure. Asshown in FIG. 5 , one or more network nodes (e.g., base station 110, acore network node, a CU, a DU, and/or an RU) may communicate with a UE(e.g., UE 120). In some aspects, the network node and the UE may be partof a wireless network (e.g., wireless network 100). The UE and thenetwork node may have established a wireless connection prior tooperations shown in FIG. 5 .

As shown by reference number 505, a network node of the one or morenetwork nodes may transmit, and the UE may receive, configurationinformation. In some aspects, the UE may receive the configurationinformation via one or more of RRC signaling, one or more medium accesscontrol (MAC) control elements (CEs), and/or downlink controlinformation (DCI), among other examples. In some aspects, theconfiguration information may include an indication of one or moreconfiguration parameters (e.g., already known to the UE and/orpreviously indicated by the network node or other network device) forselection by the UE, and/or explicit configuration information for theUE to use to configure the UE, among other examples.

In some aspects, the configuration information may indicate that the UEis to transmit capability information for using ML models. In someaspects, the configuration information may indicate that the UE is totransmit an indication of a maximum number of ML models that may becombined (e.g., configured for use during communications). Additionally,or alternatively, the configuration information may indicate that the UEis to transmit an indication of supported and/or unsupported modelcombinations. In some aspects, the configuration information mayindicate a configuration for the UE to transmit one or more indicationsof the capability information. For example, the configurationinformation may indicate that the UE is to transmit the capabilityinformation via an RRC communication. In some aspects, the configurationinformation may indicate that the UE is to transmit the capabilityinformation and a same communication or a different communication fromthe indication of the maximum number of ML models that may be combinedand/or the indication of supported and/or unsupported modelcombinations.

In some aspects, the configuration information may indicate that thecapability information is to indicate one or more performance parametersfor respective ML models based at least in part on a model combinationof additional ML models used with the respective ML models. In someaspects, the configuration information may indicate that the capabilityinformation is to indicate model combinations and one or moreperformance parameters (e.g., values for one or more performanceparameters) for one or more ML models included in the modelcombinations, with the one or more performance parameters based at leastin part on other ML models used in the model combinations. For example,the configuration information may indicate that the capabilityinformation is to indicate performance parameters for an ML model foreach of the model combinations that include the ML model. The ML modelmay have different performance parameters for different modelcombinations.

The UE may configure itself based at least in part on the configurationinformation. In some aspects, the UE may be configured to perform one ormore operations described herein based at least in part on theconfiguration information.

As shown by reference number 510, the UE may identify one or more modelcombinations of ML models (e.g., ML models associated with communicationwith the network node, an additional network node, and/or an applicationserver) that the UE supports and/or that the UE does not support. Forexample, the UE may identify a support for multiple ML models (e.g.,communication-based ML models) based at least in part on overlap ofresource demands (e.g., primarily using different processing units),based at least in part on a total resources demand that the UE supports(e.g., based at least in part on hardware and/or software of the UE),based at least in part on target network functions, and/or based atleast in part on dependency, among other examples.

In some aspects, the ML models may include one or more decision treemodels, one or more decision forest models, one or more convolutionalneural network models, one or more cluster models, one or more linearregression models, one or more feedforward neural network models, and/orone or more recurrent neural network model, among other examples. Modelcombinations may include one or more types of ML models.

As shown by reference number 515, the UE may determine one or moreperformance parameters for ML models with respect to one or more modelcombinations. For example, the UE may determine a first set of one ormore performance parameters (e.g., AI engine performance, memoryperformance, quantization (e.g., granularity of model outputs), and/ordelay in generating outputs) associated with an ML model based at leastin part on the ML model being in a first model combination.Additionally, or alternatively, the UE may determine a second set of oneor more performance parameters associated with the ML model based atleast in part on the ML model being in a second model combination (e.g.,a combination of the ML model with a different set of one or more MLmodels than the first model combination).

As shown by reference number 520, the UE may transmit, and the networknode may receive, capability information that indicates support for oneor more model combinations and/or one or more performance parameters forML models (e.g., ML models for communications) with respect to one ormore model combinations. For example, the capability information mayindicates a respective performance parameter for respective ML modelswhen used in a model combination. In some aspects, the capabilityinformation may indicate a first performance parameters for a first MLmodel in a model combination, a second performance parameter for asecond ML model in the model combination, and/or a third performanceparameter for a third ML model in the model combination, etc. In someaspects, a performance parameter for an ML model may be different whenindicated for different model combinations.

In some aspects, the capability information may indicate support for theone or more model combinations based at least in part on including anindication of supported model combinations and/or an indication ofunsupported model combinations. In some aspects, the UE may transmit thecapability information in a capability report. In some aspects, thecapability information includes, or is included in, UE radio capabilityinformation, ML capability information, and/or core network capabilityinformation. In some aspects, the UE may transmit the capabilityinformation to the first network node for forwarding to a second networknode.

In some aspects, the model combinations may be based at least in part ona first set of the ML models associated with a RAN network node, asecond set of the ML models associated with a core network network node,and/or a third set of the ML models associated with an applicationserver. In some aspects, each ML model of a model combination isassociated with only one of the RAN network node, a core network networknode, or an application server.

In some aspects, the UE may indicate the one or more performanceparameters based at least in part on including one or more indications.For example, the one or more indications may include a hash of values ofa set of performance metrics of the ML model with respect to the modelcombination, an indicator (e.g., a mapping indicator) that maps to thevalues of the set of performance metrics, or a performance class of theML model with respect to the model combination. In some aspects, theindicator may map to the values of the set of performance metrics basedat least in part on a communication protocol and/or a definition that isbased at least in part on the UE and/or the network node configured tocommunicate with the UE. In some aspects, the indicator may map to thevalues of the set of performance metrics based at least in part on adefinition that is based at least in part on a core network node and/orRAN network node (e.g., that are associated with the ML models).

In some aspects, the one or more performance parameters indicate valuesof processing resources available to apply to the ML model when the MLmodel is used in the model combination, memory resources available toapply to the ML model when the ML model is used in the modelcombination, quantization of the ML model when the ML model is used inthe model combination, and/or delay in using associated ML models in themodel combination.

In some aspects, the UE may transmit the capability information via anRRC message or another type of communication. In some aspects, the RRCmessage or other type of communication may include an indication of aset of the one or more model combinations supported or unsupported bythe UE. In some aspects, the RRC message or other type of communicationmay include an indication of a maximum number of supported ML models toinclude in a model combination. In some aspects, the RRC message orother type of communication may include an indication of a first modelcombination that includes a first indication of model parametersassociated with a first ML model of the model combination. Theindication of a first model combination may include a second indicationof model parameters associated with a second ML model of the modelcombination. In some aspects, the RRC message or other type ofcommunication may include an indication of a second model combination,that includes a third indication of model parameters associated with athird ML model of the second model combination and a fourth indicationof model parameters associated with a fourth ML model of the secondmodel combination. In some aspects, the first ML model or the second MLmodel may be a same ML model as the third ML model or the fourth MLmodel, with the same ML model having a different indication of modelparameters based at least in part on the same ML model being used in adifferent model combination. For example, the first ML model and thethird ML model may be a same ML model, the first indication of modelparameters may be associated with a first set of one or more values, thethird indication of model parameters may be associated with a second setof one or more values, and the first set of one or more values may bedifferent from the second set of one or more values.

As shown by reference number 525, the network node may forward thecapability information to an additional network node (e.g., a corenetwork node or a RAN network node) of the one or more network nodes.For example, the network node may forward the capability information theadditional network node based at least in part on the additional networknode being associated with the ML models of the one or more modelcombinations. For example, a core network node may control, support,and/or be affected by operations associated with ML models for higherlayers of communications (e.g., L3 or above). Similarly, a RAN networknode may control, support, and/or be affected by operations associatedwith ML models for lower layers of communications (e.g., L2 or below).

In some aspects, the network node may modify (e.g., add to or remove aportion of) the capability information before forwarding to theadditional network node. For example, a first portion of the capabilityinformation may be intended for the network node and a second portion ofthe capability information may be intended for the additional networknode (e.g., a core network network node or an additional RAN networknode). The network node may forward only the portion of the capabilityinformation that is intended (e.g., via a destination identificationand/or based at least in part on a type of capability information) forthe additional network node.

As shown by reference number 530, the network node may identify a modelcombination for the UE to use for communications. The network node mayidentify the model combination based at least in part on the capabilityinformation. In some aspects, the network node may identify the modelcombination to optimize communication efficiency and/or powerconsumption based at least in part on the one or more performanceparameters of the ML models of the model combination.

For example, the network node may identify a first model combinationthat, if the UE were able to provide full resources for each ML model ofthe first model combination, would provide a highest amount ofcommunication efficiency. However, based at least in part on the UEindicating that one or more of the ML models of the first modelcombination would have reduced resources available (e.g., based at leastin part on the ML models competing for resources), the first modelcombination may not provide the highest amount of communicationefficiency. For example, a second model combination may include adifferent set of ML models, which may not be as efficient as those inthe first model combination if both had full resource available, withthe UE indicating an amount of available resources for the models of thesecond model combination that would result in the second modelcombination providing a higher amount of communication efficiency thanthe first model combination. In this way, the network node may select amodel combination based at least in part on resources available to MLmodels of different model combinations and an amount of communicationefficiency gained by using the ML models with the resources available tothe ML models.

As shown by reference number 535, the UE may receive, and the networknode may transmit, an indication of the model combination. For example,the UE may receive the indication of the model combination based atleast in part on the capability information. In this way, the UE mayreceive an indication to use one or more of the ML models based at leastin part on the capability information. For example, the UE may receivean indication to use ML models that belong to the indicated modelcombination.

As shown by reference number 540, the UE and the network node maycommunicate based at least in part on the ML models of the modelcombination. In some aspects, the UE may use a selected modelcombination to configure wireless communication of the UE (e.g., a RANconfiguration). For example, based at least in part on the selectedmodel combination, the UE may configure a connection operation (e.g., arandom access channel (RACH) or physical RACH (PRACH) configuration), atraffic management operation, a timing synchronization operation, ameasurement operation, a reporting operation (e.g., a channel stateinformation (CSI) report), a reference signal configuration, a handoveroperation, and/or a resource configuration operation, among otherexamples.

In some aspects, communicating based at least in part on the ML modelsmay include applying the ML models of the model combination to improvecommunication efficiency. The UE may apply the set of ML models to oneor more network functions, such as RRM measurement, CSI reporting,and/or cell reselection, among other examples.

Based at least in part on the UE indicating performance parameters of MLmodels when used in combination with other ML models, the UE may providethe network node with an indication of performance of the ML models withimproved accuracy. In this way, the network node may configure the UE touse a combination of ML models that is based at least in part on theimproved accuracy such that performance of the ML models may besufficient to improve communication efficiency and/or conserve power,communication, network, and/or computing resources that may haveotherwise been consumed to attempt to use additional ML models withfurther reduced performance.

As indicated above, FIG. 5 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 5 . For example, oneor more of the operations described in connection with FIG. 5 may beomitted or performed by an additional node (e.g., network node or UE).

FIG. 6 is a diagram of an example 600 associated with parameters forcombinations of ML models, in accordance with the present disclosure. Inconnection with FIG. 6 , one or more network nodes (e.g., base station110, a core network node, a CU, a DU, and/or an RU) may communicate witha UE (e.g., UE 120). In some aspects, the network node and the UE may bepart of a wireless network (e.g., wireless network 100). The UE and thenetwork node may have established a wireless connection prior tooperations shown in FIG. 6 .

As shown in FIG. 6 , an indication of performance parameters 605 (e.g.,performance classes) for a combination of ML models may be based atleast in part on a mapping of the indications of performance parameters605 to a set of parameters 610 and a set of performance metric values615 of the set of parameters. For example, an indication that a first MLmodel is associated with performance indicator A (e.g., performanceclass A) when in a first model combination indicates that the first MLmodel is expected to perform with performance values 615 of the set ofparameters 610 that are associated with the performance indicator A(e.g., performance values in a same row as indicator A) when used in thefirst model combination. Similarly, an indication that the first MLmodel is associated with performance indicator C when in a second modelcombination indicates that the first ML model is expected to performwith performance values 615 of the set of parameters 610 that areassociated with the performance indicator C (e.g., performance values ina same row as indicator C) when used in the second model combination.Other performance indicators map to other combinations of performancevalues and may be associated with other ML models in one or more modelcombinations, not just a first ML model.

In some aspects, the performance parameters 605 may similarly include ahash of values of the set of parameters 610. In this way, the indicationof performance parameters 605 may use a reduced amount of overhead whencompared to an amount of overhead that would be needed to explicitlyindicate each of the set of parameters 610 without hashing. In someaspects, an indicator may include a bit value associated with theindicator or may include a bitmap with a bit associated with eachcandidate indicator.

In some aspects, the capability information may indicate performanceindicators for ML models within each indicated model combination. Forexample, the capability information may include an indication of a modelcombination set that indicates all reported model combinations. Elementsof the model combination set may be model combinations that the UEsupports. For a model combination (e.g., for each model combination),the UE may indicate a list of ML models of the model combination and oneor more model parameters. The one or more model parameters may include amodel identification for included ML models and performance indicatorsof the ML models (e.g., a performance indicator for each of the includedML models).

As indicated above, FIG. 6 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 6 .

FIG. 7 is a diagram of an example 700 associated with parameters forcombinations of ML models, in accordance with the present disclosure. Inconnection with FIG. 7 , one or more network nodes (e.g., base station110, a core network node, a CU, a DU, and/or an RU) may communicate witha UE (e.g., UE 120). In some aspects, the network node and the UE may bepart of a wireless network (e.g., wireless network 100). The UE and thenetwork node may have established a wireless connection prior tooperations shown in FIG. 7 .

As shown in FIG. 7 , the capability information may indicate that the UEsupports model combinations of up to four ML models. A model combinationlist may include a first model combination 705, a second modelcombination 710, a third model combination 715, a fourth modelcombination 720, a fifth model combination 725, and/or a sixth modelcombination 730, among other examples. In some aspects, the modelcombinations may have a uniform number of model identifications (IDs) orthe model combinations may have different numbers of model IDs. Forexample, each model combination may have a same number of model IDs orsome model combinations may have different numbers of Model IDs.

The capability information may indicate that, when used in the firstcombination 705, model identification (ID) 1 (e.g., an ML modelassociated with the model ID 1) has a performance indicator 1, model ID2 has a performance indicator 2, model ID 3 has a performance indicator3, and model ID 4 has a performance indicator 4. The performanceindicators may map to the same or different performance parameters. Forexample, performance indicators 1 and 3 may be indicator A of FIG. 6 ,performance indicator 2 may be indicator B of FIG. 6 , and/orperformance indicator 4 may be indicator C of FIG. 6 , each of which isbeing associated with a set of one or more performance values.

Similarly, the second model combination 710, the third model combination715, the fourth model combination 720, the fifth model combination 725,and/or the sixth model combination 730 may indicate model IDs of themodel combinations and associated indicators. In some aspects, the modelIDs have associated indicators that map to different performance valueswhen used in different combinations based at least in part on other MLmodels used in the different combinations. In some aspects, the modelIDs have associated indicators that map to same performance values whenused in different combinations based at least in part on other ML modelsused in the different combinations.

As indicated above, FIG. 7 is provided as an example. Other examples maydiffer from what is described with regard to FIG. 7 .

FIG. 8 is a diagram illustrating an example process 800 performed, forexample, by a UE, in accordance with the present disclosure. Exampleprocess 800 is an example where the UE (e.g., UE 120) performsoperations associated with performance indicators for combinations of MLmodels.

As shown in FIG. 8 , in some aspects, process 800 may includetransmitting capability information that indicates support for one ormore model combinations of ML models, wherein the capability informationfurther indicates one or more performance parameters of an ML model ofthe ML models with respect to a model combination of the one or moremodel combinations that includes the ML model (block 810). For example,the UE (e.g., using communication manager 140 and/or transmissioncomponent 1004, depicted in FIG. 10 ) may transmit capabilityinformation that indicates support for one or more model combinations ofML models, wherein the capability information further indicates one ormore performance parameters of an ML model of the ML models with respectto a model combination of the one or more model combinations thatincludes the ML model, as described above.

As further shown in FIG. 8 , in some aspects, process 800 may includereceiving one or more indications to use one or more of the ML modelsbased at least in part on the capability information (block 820). Forexample, the UE (e.g., using communication manager 140 and/or receptioncomponent 1002, depicted in FIG. 10 ) may receive one or moreindications to use one or more of the ML models based at least in parton the capability information, as described above.

Process 800 may include additional aspects, such as any single aspect orany combination of aspects described below and/or in connection with oneor more other processes described elsewhere herein.

In a first aspect, a performance parameter of the one or moreperformance parameters comprises one or more of a hash of values of aset of performance metrics of the ML model with respect to the modelcombination, an indicator that maps to the values of the set ofperformance metrics, or a performance class of the ML model with respectto the model combination.

In a second aspect, alone or in combination with the first aspect, theindicator maps to the values of the set of performance metrics based atleast in part on one or more of a communication protocol, or adefinition that is based at least in part on one or more of the UE or anetwork node configured to communicate with the UE.

In a third aspect, alone or in combination with one or more of the firstand second aspects, the one or more performance parameters indicatevalues for one or more of processing resources available to apply to theML model, memory resources available to apply to the ML model,quantization of the ML model, or delay in using associated ML models.

In a fourth aspect, alone or in combination with one or more of thefirst through third aspects, transmitting the capability informationcomprises transmitting the capability information via an RRC message.

In a fifth aspect, alone or in combination with one or more of the firstthrough fourth aspects, the RRC message comprises one or more of anindication of a set of the one or more model combinations, an indicationof the model combination, comprising a first indication of modelparameters associated with the ML model of the model combination,wherein the ML model is a first ML model, and a second indication ofmodel parameters associated with a second ML model of the modelcombination, or an indication of an additional model combination,comprising a third indication of model parameters associated with athird ML model of the additional model combination, and a fourthindication of model parameters associated with a fourth ML model of theadditional model combination.

In a sixth aspect, alone or in combination with one or more of the firstthrough fifth aspects, the first ML model and the third ML model are asame ML model, wherein the first indication of model parameters isassociated with a first set of one or more values, wherein the thirdindication of model parameters is associated with a second set of one ormore values, and wherein the first set of one or more values isdifferent from the second set of one or more values.

In a seventh aspect, alone or in combination with one or more of thefirst through sixth aspects, the capability information comprises one ormore of UE radio capability information, ML capability information, orcore network capability information.

In an eighth aspect, alone or in combination with one or more of thefirst through seventh aspects, transmitting the capability informationcomprises transmitting the capability information to a first networknode for forwarding to a second network node.

In a ninth aspect, alone or in combination with one or more of the firstthrough eighth aspects, the one or more model combinations of the MLmodels are based at least in part on one or more of a first set of theML models associated with a radio access network (RAN) network node, asecond set of the ML models associated with a core network (CN) networknode, or a third set of the ML models associated with an applicationserver.

In a tenth aspect, alone or in combination with one or more of the firstthrough ninth aspects, process 800 includes transmitting one or more ofan indication of supported model combinations, or an indication ofunsupported model combinations.

Although FIG. 8 shows example blocks of process 800, in some aspects,process 800 may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in FIG. 8 .Additionally, or alternatively, two or more of the blocks of process 800may be performed in parallel.

FIG. 9 is a diagram illustrating an example process 900 performed, forexample, by a network node, in accordance with the present disclosure.Example process 900 is an example where the network node (e.g., basestation 110 and/or a network node of FIG. 5 ) performs operationsassociated with performance indicators for combinations of ML models.

As shown in FIG. 9 , in some aspects, process 900 may include receivingcapability information that indicates support by a UE for one or moremodel combinations of ML models, wherein the capability informationfurther indicates one or more performance parameters of an ML model ofthe one or more ML models with respect to a model combination of the oneor more model combinations that includes the ML model (block 910). Forexample, the network node (e.g., using communication manager 150 and/orreception component 1102, depicted in FIG. 11 ) may receive capabilityinformation that indicates support by a UE for one or more modelcombinations of ML models, wherein the capability information furtherindicates one or more performance parameters of an ML model of the oneor more ML models with respect to a model combination of the one or moremodel combinations that includes the ML model, as described above.

As further shown in FIG. 9 , in some aspects, process 900 may includetransmitting one or more indications to use one or more of the ML modelsbased at least in part on the capability information (block 920). Forexample, the network node (e.g., using communication manager 150 and/ortransmission component 1104, depicted in FIG. 11 ) may transmit one ormore indications to use one or more of the ML models based at least inpart on the capability information, as described above.

Process 900 may include additional aspects, such as any single aspect orany combination of aspects described below and/or in connection with oneor more other processes described elsewhere herein.

In a first aspect, a performance parameter of the one or moreperformance parameters comprises one or more of a hash of values of aset of performance metrics of the ML model with respect to the modelcombination, an indicator that maps to the values of the set ofperformance metrics, or a performance class of the ML model with respectto the model combination.

In a second aspect, alone or in combination with the first aspect, theindicator maps to the values of the set of performance metrics based atleast in part on one or more of a communication protocol, or adefinition that is based at least in part on one or more of the UE or anetwork node configured to communicate with the UE.

In a third aspect, alone or in combination with one or more of the firstand second aspects, the one or more performance parameters indicatevalues for one or more of processing resources available to apply to theML model, memory resources available to apply to the ML model,quantization of the ML model, or delaying in using associated ML models.

In a fourth aspect, alone or in combination with one or more of thefirst through third aspects, receiving the capability informationcomprises receiving the capability information via an RRC message.

In a fifth aspect, alone or in combination with one or more of the firstthrough fourth aspects, the RRC message comprises one or more of anindication of a set of the one or more model combinations, an indicationof the model combination, comprising a first indication of modelparameters associated with the ML model of the model combination,wherein the ML model is a first ML model, and a second indication ofmodel parameters associated with a second ML model of the modelcombination, or an indication of an additional model combination,comprising a third indication of model parameters associated with athird ML model of the additional model combination, and a fourthindication of model parameters associated with a fourth ML model of theadditional model combination.

In a sixth aspect, alone or in combination with one or more of the firstthrough fifth aspects, the first ML model and the third ML model are asame ML model, wherein the first indication of model parameters isassociated with a first set of one or more values, wherein the thirdindication of model parameters is associated with a second set of one ormore values, and wherein the first set of one or more values isdifferent from the second set of one or more values.

In a seventh aspect, alone or in combination with one or more of thefirst through sixth aspects, the capability information comprises one ormore of UE radio capability information, ML capability information, orcore network capability information.

In an eighth aspect, alone or in combination with one or more of thefirst through seventh aspects, process 900 includes forwarding at leasta portion of the capability information to an additional network node.

In a ninth aspect, alone or in combination with one or more of the firstthrough eighth aspects, the one or more model combinations of the MLmodels are based at least in part on one or more of a first set of theML models associated with a RAN network node, a second set of the MLmodels associated with a CN network node, or a third set of the MLmodels associated with an application server.

In a tenth aspect, alone or in combination with one or more of the firstthrough ninth aspects, process 900 includes receiving one or more of anindication of supported model combinations, or an indication ofunsupported model combinations.

Although FIG. 9 shows example blocks of process 900, in some aspects,process 900 may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in FIG. 9 .Additionally, or alternatively, two or more of the blocks of process 900may be performed in parallel.

FIG. 10 is a diagram of an example apparatus 1000 for wirelesscommunication. The apparatus 1000 may be a UE, or a UE may include theapparatus 1000. In some aspects, the apparatus 1000 includes a receptioncomponent 1002 and a transmission component 1004, which may be incommunication with one another (for example, via one or more busesand/or one or more other components). As shown, the apparatus 1000 maycommunicate with another apparatus 1006 (such as a UE, a base station,or another wireless communication device) using the reception component1002 and the transmission component 1004. As further shown, theapparatus 1000 may include a communication manager 1008 (e.g., thecommunication manager 140). The communication manager 1008 may transmitcontrol signaling to the transmission component 1004 and/or may receivecontrol signaling via the reception component 1002 to controlcommunications of the apparatus 1000.

In some aspects, the apparatus 1000 may be configured to perform one ormore operations described herein in connection with FIGS. 5-7 .Additionally, or alternatively, the apparatus 1000 may be configured toperform one or more processes described herein, such as process 800 ofFIG. 8 . In some aspects, the apparatus 1000 and/or one or morecomponents shown in FIG. 10 may include one or more components of the UEdescribed in connection with FIG. 2 . Additionally, or alternatively,one or more components shown in FIG. 10 may be implemented within one ormore components described in connection with FIG. 2 . Additionally, oralternatively, one or more components of the set of components may beimplemented at least in part as software stored in a memory. Forexample, a component (or a portion of a component) may be implemented asinstructions or code stored in a non-transitory computer-readable mediumand executable by a controller or a processor to perform the functionsor operations of the component.

The reception component 1002 may receive communications, such asreference signals, control information, data communications, or acombination thereof, from the apparatus 1006. The reception component1002 may provide received communications to one or more other componentsof the apparatus 1000. In some aspects, the reception component 1002 mayperform signal processing on the received communications (such asfiltering, amplification, demodulation, analog-to-digital conversion,demultiplexing, deinterleaving, de-mapping, equalization, interferencecancellation, or decoding, among other examples), and may provide theprocessed signals to the one or more other components of the apparatus1000. In some aspects, the reception component 1002 may include one ormore antennas, a modem, a demodulator, a MIMO detector, a receiveprocessor, a controller/processor, a memory, or a combination thereof,of the UE described in connection with FIG. 2 .

The transmission component 1004 may transmit communications, such asreference signals, control information, data communications, or acombination thereof, to the apparatus 1006. In some aspects, one or moreother components of the apparatus 1000 may generate communications andmay provide the generated communications to the transmission component1004 for transmission to the apparatus 1006. In some aspects, thetransmission component 1004 may perform signal processing on thegenerated communications (such as filtering, amplification, modulation,digital-to-analog conversion, multiplexing, interleaving, mapping, orencoding, among other examples), and may transmit the processed signalsto the apparatus 1006. In some aspects, the transmission component 1004may include one or more antennas, a modem, a modulator, a transmit MIMOprocessor, a transmit processor, a controller/processor, a memory, or acombination thereof, of the UE described in connection with FIG. 2 . Insome aspects, the transmission component 1004 may be co-located with thereception component 1002 in a transceiver.

The transmission component 1004 may transmit capability information thatindicates support for one or more model combinations of ML models,wherein the capability information further indicates one or moreperformance parameters of an ML model of the ML models with respect to amodel combination of the one or more model combinations that includesthe ML model. The reception component 1002 may receive one or moreindications to use one or more of the ML models based at least in parton the capability information.

The transmission component 1004 may transmit one or more of anindication of supported model combinations, or an indication ofunsupported model combinations.

The number and arrangement of components shown in FIG. 10 are providedas an example. In practice, there may be additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 10 . Furthermore, two or more components shownin FIG. 10 may be implemented within a single component, or a singlecomponent shown in FIG. 10 may be implemented as multiple, distributedcomponents. Additionally, or alternatively, a set of (one or more)components shown in FIG. 10 may perform one or more functions describedas being performed by another set of components shown in FIG. 10 .

FIG. 11 is a diagram of an example apparatus 1100 for wirelesscommunication. The apparatus 1100 may be a network node, or a networknode may include the apparatus 1100. In some aspects, the apparatus 1100includes a reception component 1102 and a transmission component 1104,which may be in communication with one another (for example, via one ormore buses and/or one or more other components). As shown, the apparatus1100 may communicate with another apparatus 1106 (such as a UE, a basestation, or another wireless communication device) using the receptioncomponent 1102 and the transmission component 1104. As further shown,the apparatus 1100 may include a communication manager 1108 (e.g., thecommunication manager 150). The communication manager 1108 may transmitcontrol signaling to the transmission component 1104 and/or may receivecontrol signaling via the reception component 1102 to controlcommunications of the apparatus 1100.

In some aspects, the apparatus 1100 may be configured to perform one ormore operations described herein in connection with FIGS. 5-7 .Additionally, or alternatively, the apparatus 1100 may be configured toperform one or more processes described herein, such as process 900 ofFIG. 9 . In some aspects, the apparatus 1100 and/or one or morecomponents shown in FIG. 11 may include one or more components of thenetwork node described in connection with FIG. 2 . Additionally, oralternatively, one or more components shown in FIG. 11 may beimplemented within one or more components described in connection withFIG. 2 . Additionally, or alternatively, one or more components of theset of components may be implemented at least in part as software storedin a memory. For example, a component (or a portion of a component) maybe implemented as instructions or code stored in a non-transitorycomputer-readable medium and executable by a controller or a processorto perform the functions or operations of the component.

The reception component 1102 may receive communications, such asreference signals, control information, data communications, or acombination thereof, from the apparatus 1106. The reception component1102 may provide received communications to one or more other componentsof the apparatus 1100. In some aspects, the reception component 1102 mayperform signal processing on the received communications (such asfiltering, amplification, demodulation, analog-to-digital conversion,demultiplexing, deinterleaving, de-mapping, equalization, interferencecancellation, or decoding, among other examples), and may provide theprocessed signals to the one or more other components of the apparatus1100. In some aspects, the reception component 1102 may include one ormore antennas, a modem, a demodulator, a MIMO detector, a receiveprocessor, a controller/processor, a memory, or a combination thereof,of the network node described in connection with FIG. 2 .

The transmission component 1104 may transmit communications, such asreference signals, control information, data communications, or acombination thereof, to the apparatus 1106. In some aspects, one or moreother components of the apparatus 1100 may generate communications andmay provide the generated communications to the transmission component1104 for transmission to the apparatus 1106. In some aspects, thetransmission component 1104 may perform signal processing on thegenerated communications (such as filtering, amplification, modulation,digital-to-analog conversion, multiplexing, interleaving, mapping, orencoding, among other examples), and may transmit the processed signalsto the apparatus 1106. In some aspects, the transmission component 1104may include one or more antennas, a modem, a modulator, a transmit MIMOprocessor, a transmit processor, a controller/processor, a memory, or acombination thereof, of the network node described in connection withFIG. 2 . In some aspects, the transmission component 1104 may beco-located with the reception component 1102 in a transceiver.

The reception component 1102 may receive capability information thatindicates support by a UE for one or more model combinations of MLmodels, wherein the capability information further indicates one or moreperformance parameters of an ML model of the one or more ML models withrespect to a model combination of the one or more model combinationsthat includes the ML model. The transmission component 1104 may transmitone or more indications to use one or more of the ML models based atleast in part on the capability information.

The communication manager 1108 and/or the transmission component 1104may forward at least a portion of the capability information to anadditional network node.

The reception component 1102 may receive one or more of an indication ofsupported model combinations, or an indication of unsupported modelcombinations.

The number and arrangement of components shown in FIG. 11 are providedas an example. In practice, there may be additional components, fewercomponents, different components, or differently arranged componentsthan those shown in FIG. 11 . Furthermore, two or more components shownin FIG. 11 may be implemented within a single component, or a singlecomponent shown in FIG. 11 may be implemented as multiple, distributedcomponents. Additionally, or alternatively, a set of (one or more)components shown in FIG. 11 may perform one or more functions describedas being performed by another set of components shown in FIG. 11 .

The following provides an overview of some Aspects of the presentdisclosure:

Aspect 1: A method of wireless communication performed by a userequipment (UE), comprising: transmitting capability information thatindicates support for one or more model combinations of machine learning(ML) models, wherein the capability information further indicates one ormore performance parameters of an ML model of the ML models with respectto a model combination of the one or more model combinations thatincludes the ML model; and receiving one or more indications to use oneor more of the ML models based at least in part on the capabilityinformation.

Aspect 2: The method of Aspect 1, wherein a performance parameter of theone or more performance parameters comprises one or more of: a hash ofvalues of a set of performance metrics of the ML model with respect tothe model combination, an indicator that maps to the values of the setof performance metrics, or a performance class of the ML model withrespect to the model combination.

Aspect 3: The method of Aspect 2, wherein the indicator maps to thevalues of the set of performance metrics based at least in part on oneor more of: a communication protocol, or a definition that is based atleast in part on one or more of the UE or a network node configured tocommunicate with the UE.

Aspect 4: The method of any of Aspects 1-3, wherein the one or moreperformance parameters indicate values for one or more of: processingresources available to apply to the ML model, memory resources availableto apply to the ML model, quantization of the ML model, or delay inusing associated ML models.

Aspect 5: The method of any of Aspects 1-4, wherein transmitting thecapability information comprises: transmitting the capabilityinformation via a radio resource control (RRC) message.

Aspect 6: The method of Aspect 5, wherein the RRC message comprises oneor more of: an indication of a set of the one or more modelcombinations; an indication of the model combination, comprising: afirst indication of model parameters associated with the ML model of themodel combination, wherein the ML model is a first ML model, and asecond indication of model parameters associated with a second ML modelof the model combination, or an indication of an additional modelcombination, comprising: a third indication of model parametersassociated with a third ML model of the additional model combination,and a fourth indication of model parameters associated with a fourth MLmodel of the additional model combination.

Aspect 7: The method of Aspect 6, wherein the first ML model and thethird ML model are a same ML model, wherein the first indication ofmodel parameters is associated with a first set of one or more values,wherein the third indication of model parameters is associated with asecond set of one or more values, and wherein the first set of one ormore values is different from the second set of one or more values.

Aspect 8: The method of any of Aspects 1-7, wherein the capabilityinformation comprises one or more of: UE radio capability information,ML capability information, or core network capability information.

Aspect 9: The method of any of Aspects 1-8, wherein transmitting thecapability information comprises: transmitting the capabilityinformation to a first network node for forwarding to a second networknode.

Aspect 10: The method of any of Aspects 1-9, wherein the one or moremodel combinations of the ML models are based at least in part on one ormore of: a first set of the ML models associated with a radio accessnetwork (RAN) network node, a second set of the ML models associatedwith a core network (CN) network node, or a third set of the ML modelsassociated with an application server.

Aspect 11: The method of any of Aspects 1-10, further comprisingtransmitting one or more of: an indication of supported modelcombinations, or an indication of unsupported model combinations.

Aspect 12: A method of wireless communication performed by a networknode, comprising: receiving capability information that indicatessupport by a user equipment (UE) for one or more model combinations ofmachine learning (ML) models, wherein the capability information furtherindicates one or more performance parameters of an ML model of the oneor more ML models with respect to a model combination of the one or moremodel combinations that includes the ML model; and transmitting one ormore indications to use one or more of the ML models based at least inpart on the capability information.

Aspect 13: The method of Aspect 12, wherein a performance parameter ofthe one or more performance parameters comprises one or more of: a hashof values of a set of performance metrics of the ML model with respectto the model combination, an indicator that maps to the values of theset of performance metrics, or a performance class of the ML model withrespect to the model combination.

Aspect 14: The method of any of Aspects 12-13, wherein the indicatormaps to the values of the set of performance metrics based at least inpart on one or more of: a communication protocol, or a definition thatis based at least in part on one or more of the UE or a network nodeconfigured to communicate with the UE.

Aspect 15: The method of any of Aspects 12-14, wherein the one or moreperformance parameters indicate values for one or more of: processingresources available to apply to the ML model, memory resources availableto apply to the ML model, quantization of the ML model, or delay inusing associated ML models.

Aspect 16: The method of any of Aspects 12-15, wherein receiving thecapability information comprises: receiving the capability informationvia a radio resource control (RRC) message.

Aspect 17: The method of Aspect 16, wherein the RRC message comprisesone or more of: an indication of a set of the one or more modelcombinations; an indication of the model combination, comprising: afirst indication of model parameters associated with the ML model of themodel combination, wherein the ML model is a first ML model, and asecond indication of model parameters associated with a second ML modelof the model combination, or an indication of an additional modelcombination, comprising: a third indication of model parametersassociated with a third ML model of the additional model combination,and a fourth indication of model parameters associated with a fourth MLmodel of the additional model combination.

Aspect 18: The method of any of Aspects 17, wherein the first ML modeland the third ML model are a same ML model, wherein the first indicationof model parameters is associated with a first set of one or morevalues, wherein the third indication of model parameters is associatedwith a second set of one or more values, and wherein the first set ofone or more values is different from the second set of one or morevalues.

Aspect 19: The method of any of Aspects 12-18, wherein the capabilityinformation comprises one or more of: UE radio capability information,ML capability information, or core network capability information.

Aspect 20: The method of any of Aspects 12-19, further comprising:forwarding at least a portion of the capability information to anadditional network node.

Aspect 21: The method of any of Aspects 12-20, wherein the one or moremodel combinations of the ML models are based at least in part on one ormore of: a first set of the ML models associated with a radio accessnetwork (RAN) network node, a second set of the ML models associatedwith a core network (CN) network node, or a third set of the ML modelsassociated with an application server.

Aspect 22: The method of any of Aspects 12-21, further comprisingreceiving one or more of: an indication of supported model combinations,or an indication of unsupported model combinations.

Aspect 23: An apparatus for wireless communication at a device,comprising a processor; memory coupled with the processor; andinstructions stored in the memory and executable by the processor tocause the apparatus to perform the method of one or more of Aspects1-22.

Aspect 24: A device for wireless communication, comprising a memory andone or more processors coupled to the memory, the one or more processorsconfigured to perform the method of one or more of Aspects 1-22.

Aspect 25: An apparatus for wireless communication, comprising at leastone means for performing the method of one or more of Aspects 1-22.

Aspect 26: A non-transitory computer-readable medium storing code forwireless communication, the code comprising instructions executable by aprocessor to perform the method of one or more of Aspects 1-22.

Aspect 27: A non-transitory computer-readable medium storing a set ofinstructions for wireless communication, the set of instructionscomprising one or more instructions that, when executed by one or moreprocessors of a device, cause the device to perform the method of one ormore of Aspects 1-22.

The foregoing disclosure provides illustration and description but isnot intended to be exhaustive or to limit the aspects to the preciseforms disclosed. Modifications and variations may be made in light ofthe above disclosure or may be acquired from practice of the aspects.

As used herein, the term “component” is intended to be broadly construedas hardware and/or a combination of hardware and software. “Software”shall be construed broadly to mean instructions, instruction sets, code,code segments, program code, programs, subprograms, software modules,applications, software applications, software packages, routines,subroutines, objects, executables, threads of execution, procedures,and/or functions, among other examples, whether referred to as software,firmware, middleware, microcode, hardware description language, orotherwise. As used herein, a “processor” is implemented in hardwareand/or a combination of hardware and software. It will be apparent thatsystems and/or methods described herein may be implemented in differentforms of hardware and/or a combination of hardware and software. Theactual specialized control hardware or software code used to implementthese systems and/or methods is not limiting of the aspects. Thus, theoperation and behavior of the systems and/or methods are describedherein without reference to specific software code, since those skilledin the art will understand that software and hardware can be designed toimplement the systems and/or methods based, at least in part, on thedescription herein.

As used herein, “satisfying a threshold” may, depending on the context,refer to a value being greater than the threshold, greater than or equalto the threshold, less than the threshold, less than or equal to thethreshold, equal to the threshold, not equal to the threshold, or thelike.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various aspects. Many of thesefeatures may be combined in ways not specifically recited in the claimsand/or disclosed in the specification. The disclosure of various aspectsincludes each dependent claim in combination with every other claim inthe claim set. As used herein, a phrase referring to “at least one of” alist of items refers to any combination of those items, including singlemembers. As an example, “at least one of: a, b, or c” is intended tocover a, b, c, a+b, a+c, b+c, and a+b+c, as well as any combination withmultiples of the same element (e.g., a+a, a+a+a, a+a+b, a+a+c, a+b+b,a+c+c, b+b, b+b+b, b+b+c, c+c, and c+c+c, or any other ordering of a, b,and c).

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterms “set” and “group” are intended to include one or more items andmay be used interchangeably with “one or more.” Where only one item isintended, the phrase “only one” or similar language is used. Also, asused herein, the terms “has,” “have,” “having,” or the like are intendedto be open-ended terms that do not limit an element that they modify(e.g., an element “having” A may also have B). Further, the phrase“based on” is intended to mean “based, at least in part, on” unlessexplicitly stated otherwise. Also, as used herein, the term “or” isintended to be inclusive when used in a series and may be usedinterchangeably with “and/or,” unless explicitly stated otherwise (e.g.,if used in combination with “either” or “only one of”).

What is claimed is:
 1. A user equipment (UE) for wireless communication,comprising: a memory; and one or more processors, coupled to the memory,configured to: transmit capability information that indicates supportfor one or more model combinations of machine learning (ML) models,wherein the capability information further indicates one or moreperformance parameters of an ML model of the ML models with respect to amodel combination of the one or more model combinations that includesthe ML model; and receive one or more indications to use one or more ofthe ML models based at least in part on the capability information. 2.The UE of claim 1, wherein a performance parameter of the one or moreperformance parameters comprises one or more of: a hash of values of aset of performance metrics of the ML model with respect to the modelcombination, an indicator that maps to the values of the set ofperformance metrics, or a performance class of the ML model with respectto the model combination.
 3. The UE of claim 2, wherein the indicatormaps to the values of the set of performance metrics based at least inpart on one or more of: a communication protocol, or a definition thatis based at least in part on one or more of the UE or a network nodeconfigured to communicate with the UE.
 4. The UE of claim 1, wherein theone or more performance parameters indicate values for one or more of:processing resources available to apply to the ML model, memoryresources available to apply to the ML model, quantization of the MLmodel, or delay in using associated ML models.
 5. The UE of claim 1,wherein the one or more processors, to transmit the capabilityinformation, are configured to: transmit the capability information viaa radio resource control (RRC) message.
 6. The UE of claim 5, whereinthe RRC message comprises one or more of: an indication of a set of theone or more model combinations; an indication of the model combination,comprising: a first indication of model parameters associated with theML model of the model combination, wherein the ML model is a first MLmodel, and a second indication of model parameters associated with asecond ML model of the model combination, or an indication of anadditional model combination, comprising: a third indication of modelparameters associated with a third ML model of the second modelcombination, and a fourth indication of model parameters associated witha fourth ML model of the additional model combination.
 7. The UE ofclaim 6, wherein the first ML model and the third ML model are a same MLmodel, wherein the first indication of model parameters is associatedwith a first set of one or more values, wherein the third indication ofmodel parameters is associated with a second set of one or more values,and wherein the first set of one or more values is different from thesecond set of one or more values.
 8. The UE of claim 1, wherein thecapability information comprises one or more of: UE radio capabilityinformation, ML capability information, or core network capabilityinformation.
 9. The UE of claim 1, wherein the one or more processors,to transmit the capability information, are configured to: transmit thecapability information to a first network node for forwarding to asecond network node.
 10. The UE of claim 1, wherein the one or moremodel combinations of the ML models are based at least in part on one ormore of: a first set of the ML models associated with a radio accessnetwork (RAN) network node, a second set of the ML models associatedwith a core network (CN) network node, or a third set of the ML modelsassociated with an application server.
 11. The UE of claim 1, whereinthe one or more processors are further configured to transmit one ormore of: an indication of supported model combinations, or an indicationof unsupported model combinations.
 12. A network node for wirelesscommunication, comprising: a memory; and one or more processors, coupledto the memory, configured to: receive capability information thatindicates support by a user equipment (UE) for one or more modelcombinations of machine learning (ML) models, wherein the capabilityinformation further indicates one or more performance parameters of anML model of the one or more ML models with respect to a modelcombination of the one or more model combinations that includes the MLmodel; and transmit one or more indications to use one or more of the MLmodels based at least in part on the capability information.
 13. Thenetwork node of claim 12, wherein a performance parameter of the one ormore performance parameters comprises one or more of: a hash of valuesof a set of performance metrics of the ML model with respect to themodel combination, an indicator that maps to the values of the set ofperformance metrics, or a performance class of the ML model with respectto the model combination.
 14. The network node of claim 13, wherein theindicator maps to the values of the set of performance metrics based atleast in part on one or more of: a communication protocol, or adefinition that is based at least in part on one or more of the UE or anetwork node configured to communicate with the UE.
 15. The network nodeof claim 12, wherein the one or more performance parameters indicatevalues for one or more of: processing resources available to apply tothe ML model, memory resources available to apply to the ML model,quantization of the ML model, or delay in using associated ML models.16. The network node of claim 12, wherein the one or more processors, toreceive the capability information, are configured to: transmit thecapability information via a radio resource control (RRC) message. 17.The network node of claim 16, wherein the RRC message comprises one ormore of: an indication of a set of the one or more model combinations;an indication of the model combination, comprising: a first indicationof model parameters associated with the ML model of the modelcombination, wherein the ML model is a first ML model, and a secondindication of model parameters associated with a second ML model of themodel combination, or an indication of an additional model combination,comprising: a third indication of model parameters associated with athird ML model of the second model combination, and a fourth indicationof model parameters associated with a fourth ML model of the secondmodel combination.
 18. The network node of claim 17, wherein the firstML model and the third ML model are a same ML model, wherein the firstindication of model parameters is associated with a first set of one ormore values, wherein the third indication of model parameters isassociated with a second set of one or more values, and wherein thefirst set of one or more values is different from the second set of oneor more values.
 19. The network node of claim 12, wherein the capabilityinformation comprises one or more of: UE radio capability information,ML capability information, or core network capability information. 20.The network node of claim 12, wherein the one or more processors arefurther configured to: forward at least a portion of the capabilityinformation to an additional network node.
 21. The network node of claim12, wherein the one or more model combinations of the ML models arebased at least in part on one or more of: a first set of the ML modelsassociated with a radio access network (RAN) network node, a second setof the ML models associated with a core network (CN) network node, or athird set of the ML models associated with an application server. 22.The network node of claim 12, wherein the one or more processors arefurther configured to receive one or more of: an indication of supportedmodel combinations, or an indication of unsupported model combinations.23. A method of wireless communication performed by a user equipment(UE), comprising: transmitting capability information that indicatessupport for one or more model combinations of machine learning (ML)models, wherein the capability information further indicates one or moreperformance parameters of an ML model of the ML models with respect to amodel combination of the one or more model combinations that includesthe ML model; and receiving one or more indications to use one or moreof the ML models based at least in part on the capability information.24. The method of claim 23, wherein a performance parameter of the oneor more performance parameters comprises one or more of: a hash ofvalues of a set of performance metrics of the ML model with respect tothe model combination, an indicator that maps to the values of the setof performance metrics, or a performance class of the ML model withrespect to the model combination.
 25. The method of claim 23, whereintransmitting the capability information comprises: transmitting thecapability information via a radio resource control (RRC) message. 26.The method of claim 25, wherein the RRC message comprises one or moreof: an indication of a set of the one or more model combinations; anindication of the model combination, comprising: a first indication ofmodel parameters associated with the ML model of the model combination,wherein the ML model is a first ML model, and a second indication ofmodel parameters associated with a second ML model of the modelcombination, or an indication of an additional model combination,comprising: a third indication of model parameters associated with athird ML model of the second model combination, and a fourth indicationof model parameters associated with a fourth ML model of the secondmodel combination.
 27. A method of wireless communication performed by anetwork node, comprising: receiving capability information thatindicates support by a user equipment (UE) for one or more modelcombinations of machine learning (ML) models, wherein the capabilityinformation further indicates one or more performance parameters of anML model of the one or more ML models with respect to a modelcombination of the one or more model combinations that includes the MLmodel; and transmitting one or more indications to use one or more ofthe ML models based at least in part on the capability information. 28.The method of claim 27, wherein a performance parameter of the one ormore performance parameters comprises one or more of: a hash of valuesof a set of performance metrics of the ML model with respect to themodel combination, an indicator that maps to the values of the set ofperformance metrics, or a performance class of the ML model with respectto the model combination.
 29. The method of claim 27, wherein receivingthe capability information comprises: transmitting the capabilityinformation via a radio resource control (RRC) message.
 30. The methodof claim 29, wherein the RRC message comprises one or more of: anindication of a set of the one or more model combinations; an indicationof the model combination, comprising: a first indication of modelparameters associated with the ML model of the model combination,wherein the ML model is a first ML model, and a second indication ofmodel parameters associated with a second ML model of the modelcombination, or an indication of an additional model combination,comprising: a third indication of model parameters associated with athird ML model of the second model combination, and a fourth indicationof model parameters associated with a fourth ML model of the secondmodel combination.